Accurate measurements of structures, such as all or part of a building and associated areas, are relevant for the construction, remodeling, roofing, building envelope modeling, landscaping, insurance, Virtual Reality/Augmented Reality, and gaming industries. For example, such information is often used by professionals to create quotes, support field planning, make design decisions, measure inspected items, and specify materials and the costs for both new building constructions, as well as for remodeling or repair of existing structures. Landscape designers likewise require accurate measurements. In the insurance industry, accurate information about costs is critical to determining the appropriate premiums for insuring buildings/structures, as well as the reimbursements due to a policyholder resulting from a claim.
As a specific, but non-limiting example, the construction and insurance industries require accurate measurements of various parameters related to roof structures, such as roof dimensions, pitches, penetrations, conditions, obstructions, surfaces, areas, vertex type (e.g., roof corner, vent count, etc.), edge types, surface types (e.g., grey asphalt), and associated material costs. Traditionally, measurements of structures, such as a building roof, have been done by hand. In order to obtain accurate measurements of a building, a worker would be required to visit the building site to take and record roof measurements by hand. To ensure accuracy, the worker would be required to climb onto the roof to obtain measurements, thus leading to potential injuries. Complex roof configurations would result in less accuracy and/or more danger to the worker. In some cases, it is not even feasible to climb a ladder and measure on the roof due to high pitch (>10/12) or when a roof contains damaged and unstable sections. Lastly, in many roofing situations, time can be of the essence: a damaged roof must be replaced quickly to reduce or prevent damage to the interior of the building and its contents. This means that the contractor that can return a roofing quote estimate the quickest might expect to be hired for the job, whereas those submitting bids later will lose the opportunity to gain the revenue.
Obtaining highly accurate measurements using manual techniques such as tape measurement devices is very difficult to achieve on large structures such as roofs, buildings, facades, roads, etc. When survey or field crews go out to capture the measurements on a building, typically, the field sketch will be documented on paper. Validating the accuracy of such measurements is even more challenging because there is no verifiable relationship of the field sketch back to the original structure, unless photos were taken at each endpoint of each measurement. This means that if a different measuring crew or even the same crew re-measures the structure, there is a very high probability that the two sets of measurements will be different. Unambiguously establishing relevant endpoints in a repeatable manner could enable comparison to confirm accuracy, possibly with a high precision measurement device, such as a laser scanner. Another way to indicate the endpoints is to physically mark each measurement endpoint on the structure. However, there is no way in either case to confirm that the measurements were actually taken from those endpoints on the physical structure. In short, such manual in-field measurements do not provide high confidence in the results because typically the user cannot verify the accuracy of the provided measurements. This means that any field measurement errors cannot be verified correctly (i.e., with high precision) after they are taken, so it is very common for these errors to translate through inflated quotes and material cost, resulting in waste and reduced profit margin for the project.
Given at least the concerns of accuracy, time, and costs issues, much effort has been directed toward improving the generation of measurements and the related cost and labor estimates of roofs. To this end, various methods have been proposed to generate roofing measurements without requiring a worker to visit the job site to perform measurements but, rather, to provide measurements from remote locations. Several manual methods have been proposed for this endeavor using aerial images of a roof from which measurements can be derived, as set out, for example, in the background section of US Patent Application Publication No. 20110187713, the disclosure of which is incorporated herein in its entirety by this reference. Such methods are time consuming, require highly trained personnel and/or do not provide highly accurate results. To reduce the time and effort needed to generate wireframes and related output direct from aerial images, the '713 Publication proposes a semi-automated processing methodology. U.S. Pat. No. 9,501,700, the disclosure of which is incorporated in its entirety by this reference, purports to provide a fully automated method of processing aerial images to generate roof models or roofing estimates. However, neither of these automated methods has obtained widespread use as of today because, quite simply, accurate results cannot be assured. Again, the confidence in the output of such methods is not high.
In contrast to the direct output from aerial image methodologies set out in the '713 Publication and the '700 Patent, point cloud information can be used to generate roof measurements having improved characteristics. Such improvements are due, at least in part, to the point clouds being more likely to comprise useable 3D information, as compared to the direct-from-image methods from which 3D information need to be extracted from two or more images after establishing correspondences among similar points in images. However, current methodologies do not readily allow point clouds that comprise structural information to be processed automatically to generate accurate wireframes that can be used as output suitable for roof models, measurements, estimates etc. As a result, such point cloud information must undergo substantial manual processing effort to produce a suitable wireframe that can provide useful outputs such as measurements, estimates, etc. This is generally accomplished by importing a point cloud into a 3D CAD tool (for example, AutoCAD®, Revit®, etc.) followed by time consuming manual manipulation of the wireframe to generate a suitably accurate wireframe. Often, visually finding 3D points to build a wireframe on a point cloud while zoomed in is challenging due to loss of perspective for the overall structure. Moreover, even with such extensive manual manipulation, accuracy of output, for example measurements and/or geometric information, that are derivable from such point clouds cannot be guaranteed. In short, existing methods of processing point clouds to generate wireframes and related output do not provide high user confidence in the output.
Notably, any errors in the original wireframe output will be propagated throughout direct and indirect use of the output. For example, if a measurement derived from the output deviates from the actual measurement, any construction estimate generated from that measurement will also deviate from the actual requirement. Overestimation of materials and labor due to uncertainties in measurement confidence is a common process to ensure enough material is on site to complete the job and avoid delays in shipping of new material. Further, if the deviation is in regard to a roof pitch, the measurement mismatch, and any associated downsides, between the actual roof measurement and the roof measurement generated from an automated or manual output will be exacerbated.
Put another way, existing methods of generating wireframes and associated output from structures or elements of interest, such as roofs, are time consuming and known to typically generate inaccurate results, whether they be in the form of models, measurements, geometric information, construction estimates, reports, manual sketches etc. Such known inaccuracy results in a lack of user confidence in the results, and attendant compensation, such as by an additional validation or re-measurement step and/or increased labor and material costs, are needed to counter negative outcomes related thereto.
In addition to being able to generate wireframes from which accurate measurements can be derived, current methods of generating wireframes do not provide users with any degree of confidence that the wireframe and any associated output therefrom are accurate representations of the structure or element of interest. In short, users of wireframes are required to trust that the wireframe and any associated output are, in fact, what they purport to be. Given the stakes involved in such reliance, users will typically make educated guesses at how incorrect the wireframes and associated outputs are, thus resulting in downsides such as ordering extra supplies “just in case.”
Moreover, in some cases, a wireframe and associated output that are only marginally accurate, may be “good enough” for some purposes. Users nonetheless are not provided with any knowledge of how correct or incorrect the wireframe and associated output might be in a particular situation. In other words, inaccurate wireframes are indistinguishable from accurate wireframes as outputs from the vantage point of the users. Verification that a wireframe is “good enough” can be relevant in some user contexts, but there is no current methodology that provides the information needed to generate such user confidence in the nature and quality of a generated wireframe and associated output therefrom.
There remains a need for reducing and/or streamlining the amount of effort currently required to generate accurate wireframes of structures or elements of interest. Still further, there is a need for improvements in the ability to generate user confidence in the information derivable from such wireframes and output generated therefrom. The present disclosure provides these and other benefits.